Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Text1
Numeric10
Categorical4

Alerts

user_id has unique valuesUnique
average_login_time has unique valuesUnique
average_time_per_learning_session has unique valuesUnique
recent_learning_achievement has unique valuesUnique
abandoned_learning_sessions has 464 (4.6%) zerosZeros
customer_inquiry_history has 1376 (13.8%) zerosZeros
payment_pattern has 1275 (12.8%) zerosZeros

Reproduction

Analysis started2024-03-31 12:48:22.694373
Analysis finished2024-03-31 12:48:39.682120
Duration16.99 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

user_id
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:39.921809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowb919c29d
2nd rowa0a60abb
3rd rowb9f171ae
4th row5dc0ba8b
5th row65c83654
ValueCountFrequency (%)
b919c29d 1
 
< 0.1%
9e6713d2 1
 
< 0.1%
26d9ebe2 1
 
< 0.1%
b9f171ae 1
 
< 0.1%
5dc0ba8b 1
 
< 0.1%
65c83654 1
 
< 0.1%
b7586b82 1
 
< 0.1%
682ca511 1
 
< 0.1%
8c948022 1
 
< 0.1%
0eac44aa 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-31T21:48:40.468871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5125
 
6.4%
2 5123
 
6.4%
6 5098
 
6.4%
9 5096
 
6.4%
d 5084
 
6.4%
a 5065
 
6.3%
f 5022
 
6.3%
7 4987
 
6.2%
5 4985
 
6.2%
3 4980
 
6.2%
Other values (6) 29435
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50086
62.6%
Lowercase Letter 29914
37.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5125
10.2%
2 5123
10.2%
6 5098
10.2%
9 5096
10.2%
7 4987
10.0%
5 4985
10.0%
3 4980
9.9%
8 4951
9.9%
4 4939
9.9%
1 4802
9.6%
Lowercase Letter
ValueCountFrequency (%)
d 5084
17.0%
a 5065
16.9%
f 5022
16.8%
e 4965
16.6%
b 4899
16.4%
c 4879
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 50086
62.6%
Latin 29914
37.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5125
10.2%
2 5123
10.2%
6 5098
10.2%
9 5096
10.2%
7 4987
10.0%
5 4985
10.0%
3 4980
9.9%
8 4951
9.9%
4 4939
9.9%
1 4802
9.6%
Latin
ValueCountFrequency (%)
d 5084
17.0%
a 5065
16.9%
f 5022
16.8%
e 4965
16.6%
b 4899
16.4%
c 4879
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5125
 
6.4%
2 5123
 
6.4%
6 5098
 
6.4%
9 5096
 
6.4%
d 5084
 
6.4%
a 5065
 
6.3%
f 5022
 
6.3%
7 4987
 
6.2%
5 4985
 
6.2%
3 4980
 
6.2%
Other values (6) 29435
36.8%

subscription_duration
Real number (ℝ)

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8974
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:40.692274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile22
Maximum23
Range22
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.6008962
Coefficient of variation (CV)0.55481838
Kurtosis-1.1984552
Mean11.8974
Median Absolute Deviation (MAD)6
Skewness0.018707638
Sum118974
Variance43.57183
MonotonicityNot monotonic
2024-03-31T21:48:40.832452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4 475
 
4.8%
6 471
 
4.7%
18 464
 
4.6%
12 461
 
4.6%
16 448
 
4.5%
10 448
 
4.5%
2 443
 
4.4%
9 440
 
4.4%
8 438
 
4.4%
13 437
 
4.4%
Other values (13) 5475
54.8%
ValueCountFrequency (%)
1 436
4.4%
2 443
4.4%
3 408
4.1%
4 475
4.8%
5 424
4.2%
6 471
4.7%
7 432
4.3%
8 438
4.4%
9 440
4.4%
10 448
4.5%
ValueCountFrequency (%)
23 410
4.1%
22 414
4.1%
21 433
4.3%
20 412
4.1%
19 423
4.2%
18 464
4.6%
17 429
4.3%
16 448
4.5%
15 408
4.1%
14 418
4.2%

recent_login_time
Real number (ℝ)

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0132
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:41.066032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3625725
Coefficient of variation (CV)0.55701466
Kurtosis-1.1946935
Mean15.0132
Median Absolute Deviation (MAD)7
Skewness-0.0067724569
Sum150132
Variance69.932619
MonotonicityNot monotonic
2024-03-31T21:48:41.312545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
19 380
 
3.8%
27 370
 
3.7%
14 366
 
3.7%
12 365
 
3.6%
13 363
 
3.6%
6 362
 
3.6%
20 362
 
3.6%
29 359
 
3.6%
26 359
 
3.6%
5 355
 
3.5%
Other values (19) 6359
63.6%
ValueCountFrequency (%)
1 347
3.5%
2 350
3.5%
3 349
3.5%
4 332
3.3%
5 355
3.5%
6 362
3.6%
7 322
3.2%
8 322
3.2%
9 350
3.5%
10 335
3.4%
ValueCountFrequency (%)
29 359
3.6%
28 313
3.1%
27 370
3.7%
26 359
3.6%
25 323
3.2%
24 328
3.3%
23 338
3.4%
22 349
3.5%
21 333
3.3%
20 362
3.6%

average_login_time
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.994076
Minimum2.3661894
Maximum26.99849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:41.492455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3661894
5-th percentile10.082908
Q113.025597
median14.979228
Q316.99534
95-th percentile19.889157
Maximum26.99849
Range24.632301
Interquartile range (IQR)3.9697434

Descriptive statistics

Standard deviation3.0018689
Coefficient of variation (CV)0.20020365
Kurtosis0.12637715
Mean14.994076
Median Absolute Deviation (MAD)1.9797721
Skewness-0.00091709901
Sum149940.76
Variance9.0112167
MonotonicityNot monotonic
2024-03-31T21:48:41.915141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.94616267 1
 
< 0.1%
12.14205551 1
 
< 0.1%
12.96703355 1
 
< 0.1%
16.92823888 1
 
< 0.1%
16.11113579 1
 
< 0.1%
12.80250802 1
 
< 0.1%
14.33511959 1
 
< 0.1%
12.93596736 1
 
< 0.1%
18.23230944 1
 
< 0.1%
17.75482991 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2.366189404 1
< 0.1%
3.375170044 1
< 0.1%
3.483284975 1
< 0.1%
3.636424091 1
< 0.1%
3.969999827 1
< 0.1%
4.57854295 1
< 0.1%
4.632367511 1
< 0.1%
4.72938013 1
< 0.1%
4.735407162 1
< 0.1%
4.903691067 1
< 0.1%
ValueCountFrequency (%)
26.99849039 1
< 0.1%
26.28439609 1
< 0.1%
26.12559619 1
< 0.1%
25.81583339 1
< 0.1%
25.4085567 1
< 0.1%
25.27951372 1
< 0.1%
25.27128872 1
< 0.1%
25.10060844 1
< 0.1%
24.96267863 1
< 0.1%
24.70116761 1
< 0.1%

average_time_per_learning_session
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.91772
Minimum0.011514797
Maximum503.37262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:42.253089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.011514797
5-th percentile2.8593666
Q115.276611
median37.578818
Q375.5842
95-th percentile167.14456
Maximum503.37262
Range503.3611
Interquartile range (IQR)60.307589

Descriptive statistics

Standard deviation56.02431
Coefficient of variation (CV)1.02015
Kurtosis6.5379223
Mean54.91772
Median Absolute Deviation (MAD)26.307528
Skewness2.0967309
Sum549177.2
Variance3138.7234
MonotonicityNot monotonic
2024-03-31T21:48:42.539759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.427187446 1
 
< 0.1%
4.434853526 1
 
< 0.1%
66.13575799 1
 
< 0.1%
47.01976854 1
 
< 0.1%
81.94611791 1
 
< 0.1%
31.08602976 1
 
< 0.1%
94.55224724 1
 
< 0.1%
30.46083964 1
 
< 0.1%
76.94902379 1
 
< 0.1%
8.395000851 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.01151479687 1
< 0.1%
0.01256244985 1
< 0.1%
0.01353515704 1
< 0.1%
0.01747294109 1
< 0.1%
0.02955498291 1
< 0.1%
0.03593061607 1
< 0.1%
0.03765683581 1
< 0.1%
0.0450142908 1
< 0.1%
0.05841939652 1
< 0.1%
0.06064034153 1
< 0.1%
ValueCountFrequency (%)
503.3726162 1
< 0.1%
490.2334431 1
< 0.1%
470.2894984 1
< 0.1%
447.0958436 1
< 0.1%
444.3240582 1
< 0.1%
434.4525606 1
< 0.1%
431.4303375 1
< 0.1%
430.8502601 1
< 0.1%
411.1092605 1
< 0.1%
408.3664533 1
< 0.1%
Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5454
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:43.116419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9322395
Coefficient of variation (CV)0.55257222
Kurtosis-1.2140416
Mean12.5454
Median Absolute Deviation (MAD)6
Skewness0.0048195819
Sum125454
Variance48.055944
MonotonicityNot monotonic
2024-03-31T21:48:43.354638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
24 459
 
4.6%
8 452
 
4.5%
20 447
 
4.5%
4 446
 
4.5%
10 435
 
4.3%
17 433
 
4.3%
19 428
 
4.3%
5 423
 
4.2%
12 423
 
4.2%
7 422
 
4.2%
Other values (14) 5632
56.3%
ValueCountFrequency (%)
1 395
4.0%
2 397
4.0%
3 412
4.1%
4 446
4.5%
5 423
4.2%
6 406
4.1%
7 422
4.2%
8 452
4.5%
9 415
4.2%
10 435
4.3%
ValueCountFrequency (%)
24 459
4.6%
23 396
4.0%
22 405
4.0%
21 416
4.2%
20 447
4.5%
19 428
4.3%
18 401
4.0%
17 433
4.3%
16 389
3.9%
15 420
4.2%

total_completed_courses
Real number (ℝ)

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.2275
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:43.692797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median12
Q315
95-th percentile18
Maximum27
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.634125
Coefficient of variation (CV)0.29720916
Kurtosis0.052459229
Mean12.2275
Median Absolute Deviation (MAD)2
Skewness0.23450829
Sum122275
Variance13.206864
MonotonicityNot monotonic
2024-03-31T21:48:44.011811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 1105
11.1%
11 1064
10.6%
13 1023
10.2%
10 944
9.4%
14 936
9.4%
9 806
8.1%
15 783
7.8%
8 599
 
6.0%
16 563
 
5.6%
17 471
 
4.7%
Other values (17) 1706
17.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 10
 
0.1%
3 23
 
0.2%
4 60
 
0.6%
5 155
 
1.6%
6 238
 
2.4%
7 442
4.4%
8 599
6.0%
9 806
8.1%
10 944
9.4%
ValueCountFrequency (%)
27 3
 
< 0.1%
26 5
 
0.1%
25 7
 
0.1%
24 12
 
0.1%
23 27
 
0.3%
22 30
 
0.3%
21 62
 
0.6%
20 115
 
1.1%
19 190
1.9%
18 326
3.3%

recent_learning_achievement
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.029513
Minimum35.941755
Maximum112.64383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:44.361816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.941755
5-th percentile58.398116
Q168.278054
median75.126061
Q381.718976
95-th percentile91.303072
Maximum112.64383
Range76.702073
Interquartile range (IQR)13.440921

Descriptive statistics

Standard deviation9.9685287
Coefficient of variation (CV)0.13286143
Kurtosis0.010797593
Mean75.029513
Median Absolute Deviation (MAD)6.7363223
Skewness-0.033336809
Sum750295.13
Variance99.371564
MonotonicityNot monotonic
2024-03-31T21:48:44.751483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.36045451 1
 
< 0.1%
70.84653006 1
 
< 0.1%
67.53440552 1
 
< 0.1%
62.53942778 1
 
< 0.1%
52.5271114 1
 
< 0.1%
82.25935394 1
 
< 0.1%
81.86058116 1
 
< 0.1%
65.41001964 1
 
< 0.1%
82.13299538 1
 
< 0.1%
71.07851528 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
35.94175494 1
< 0.1%
36.11556248 1
< 0.1%
36.83097921 1
< 0.1%
41.7529423 1
< 0.1%
42.25486282 1
< 0.1%
42.43085788 1
< 0.1%
42.87024809 1
< 0.1%
42.97021654 1
< 0.1%
43.06818451 1
< 0.1%
43.41086054 1
< 0.1%
ValueCountFrequency (%)
112.6438275 1
< 0.1%
111.2196475 1
< 0.1%
109.6868515 1
< 0.1%
108.1270993 1
< 0.1%
107.1351532 1
< 0.1%
106.9429266 1
< 0.1%
106.3855119 1
< 0.1%
106.1022296 1
< 0.1%
105.3933076 1
< 0.1%
105.3180014 1
< 0.1%

abandoned_learning_sessions
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0436
Minimum0
Maximum12
Zeros464
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:45.100625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7550519
Coefficient of variation (CV)0.57663683
Kurtosis0.3979902
Mean3.0436
Median Absolute Deviation (MAD)1
Skewness0.60186619
Sum30436
Variance3.0802071
MonotonicityNot monotonic
2024-03-31T21:48:45.318926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 2226
22.3%
3 2195
21.9%
4 1704
17.0%
1 1485
14.8%
5 1026
10.3%
6 530
 
5.3%
0 464
 
4.6%
7 237
 
2.4%
8 91
 
0.9%
9 27
 
0.3%
Other values (3) 15
 
0.1%
ValueCountFrequency (%)
0 464
 
4.6%
1 1485
14.8%
2 2226
22.3%
3 2195
21.9%
4 1704
17.0%
5 1026
10.3%
6 530
 
5.3%
7 237
 
2.4%
8 91
 
0.9%
9 27
 
0.3%
ValueCountFrequency (%)
12 2
 
< 0.1%
11 4
 
< 0.1%
10 9
 
0.1%
9 27
 
0.3%
8 91
 
0.9%
7 237
 
2.4%
6 530
 
5.3%
5 1026
10.3%
4 1704
17.0%
3 2195
21.9%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
5
4564 
4
2070 
3
1588 
2
1219 
1
559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%

Length

2024-03-31T21:48:45.574926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-31T21:48:45.884603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%

Most occurring characters

ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4564
45.6%
4 2070
20.7%
3 1588
 
15.9%
2 1219
 
12.2%
1 559
 
5.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Low
4992 
Medium
3008 
High
2000 

Length

Max length6
Median length4
Mean length4.1024
Min length3

Characters and Unicode

Total characters41024
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowMedium
3rd rowMedium
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Low 4992
49.9%
Medium 3008
30.1%
High 2000
20.0%

Length

2024-03-31T21:48:46.138808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-31T21:48:46.309752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
low 4992
49.9%
medium 3008
30.1%
high 2000
20.0%

Most occurring characters

ValueCountFrequency (%)
i 5008
12.2%
L 4992
12.2%
o 4992
12.2%
w 4992
12.2%
M 3008
7.3%
e 3008
7.3%
d 3008
7.3%
u 3008
7.3%
m 3008
7.3%
H 2000
 
4.9%
Other values (2) 4000
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31024
75.6%
Uppercase Letter 10000
 
24.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5008
16.1%
o 4992
16.1%
w 4992
16.1%
e 3008
9.7%
d 3008
9.7%
u 3008
9.7%
m 3008
9.7%
g 2000
 
6.4%
h 2000
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
L 4992
49.9%
M 3008
30.1%
H 2000
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5008
12.2%
L 4992
12.2%
o 4992
12.2%
w 4992
12.2%
M 3008
7.3%
e 3008
7.3%
d 3008
7.3%
u 3008
7.3%
m 3008
7.3%
H 2000
 
4.9%
Other values (2) 4000
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5008
12.2%
L 4992
12.2%
o 4992
12.2%
w 4992
12.2%
M 3008
7.3%
e 3008
7.3%
d 3008
7.3%
u 3008
7.3%
m 3008
7.3%
H 2000
 
4.9%
Other values (2) 4000
9.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Basic
5979 
Premium
4021 

Length

Max length7
Median length5
Mean length5.8042
Min length5

Characters and Unicode

Total characters58042
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBasic
2nd rowBasic
3rd rowPremium
4th rowBasic
5th rowBasic

Common Values

ValueCountFrequency (%)
Basic 5979
59.8%
Premium 4021
40.2%

Length

2024-03-31T21:48:46.554486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-31T21:48:46.706514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
basic 5979
59.8%
premium 4021
40.2%

Most occurring characters

ValueCountFrequency (%)
i 10000
17.2%
m 8042
13.9%
B 5979
10.3%
a 5979
10.3%
s 5979
10.3%
c 5979
10.3%
P 4021
6.9%
r 4021
6.9%
e 4021
6.9%
u 4021
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48042
82.8%
Uppercase Letter 10000
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 10000
20.8%
m 8042
16.7%
a 5979
12.4%
s 5979
12.4%
c 5979
12.4%
r 4021
8.4%
e 4021
8.4%
u 4021
8.4%
Uppercase Letter
ValueCountFrequency (%)
B 5979
59.8%
P 4021
40.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 58042
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 10000
17.2%
m 8042
13.9%
B 5979
10.3%
a 5979
10.3%
s 5979
10.3%
c 5979
10.3%
P 4021
6.9%
r 4021
6.9%
e 4021
6.9%
u 4021
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 10000
17.2%
m 8042
13.9%
B 5979
10.3%
a 5979
10.3%
s 5979
10.3%
c 5979
10.3%
P 4021
6.9%
r 4021
6.9%
e 4021
6.9%
u 4021
6.9%

customer_inquiry_history
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0105
Minimum0
Maximum10
Zeros1376
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:46.916849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4209826
Coefficient of variation (CV)0.70678072
Kurtosis0.35340642
Mean2.0105
Median Absolute Deviation (MAD)1
Skewness0.66549885
Sum20105
Variance2.0191917
MonotonicityNot monotonic
2024-03-31T21:48:47.105245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2668
26.7%
2 2645
26.5%
3 1851
18.5%
0 1376
13.8%
4 917
 
9.2%
5 385
 
3.9%
6 117
 
1.2%
7 31
 
0.3%
8 9
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 1376
13.8%
1 2668
26.7%
2 2645
26.5%
3 1851
18.5%
4 917
 
9.2%
5 385
 
3.9%
6 117
 
1.2%
7 31
 
0.3%
8 9
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 9
 
0.1%
7 31
 
0.3%
6 117
 
1.2%
5 385
 
3.9%
4 917
 
9.2%
3 1851
18.5%
2 2645
26.5%
1 2668
26.7%
0 1376
13.8%

payment_pattern
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5029
Minimum0
Maximum7
Zeros1275
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-03-31T21:48:47.275369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3112606
Coefficient of variation (CV)0.65981348
Kurtosis-1.2606191
Mean3.5029
Median Absolute Deviation (MAD)2
Skewness-0.0019318938
Sum35029
Variance5.3419258
MonotonicityNot monotonic
2024-03-31T21:48:47.498228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1285
12.8%
7 1284
12.8%
0 1275
12.8%
6 1268
12.7%
5 1238
12.4%
3 1230
12.3%
4 1214
12.1%
2 1206
12.1%
ValueCountFrequency (%)
0 1275
12.8%
1 1285
12.8%
2 1206
12.1%
3 1230
12.3%
4 1214
12.1%
5 1238
12.4%
6 1268
12.7%
7 1284
12.8%
ValueCountFrequency (%)
7 1284
12.8%
6 1268
12.7%
5 1238
12.4%
4 1214
12.1%
3 1230
12.3%
2 1206
12.1%
1 1285
12.8%
0 1275
12.8%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
6199 
0
3801 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Length

2024-03-31T21:48:47.736303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-31T21:48:47.899132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Most occurring characters

ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6199
62.0%
0 3801
38.0%

Interactions

2024-03-31T21:48:37.331672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.504118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.288836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.107887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.012169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:27.500917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.400813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:31.347314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:33.770628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.603839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.528988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.585974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.362148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.182637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.103055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:27.695300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.512342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:31.551677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:33.963554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.782268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.657369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.668076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.445236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.254568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.183893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:27.871157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.715713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:31.809274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:34.137231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.987612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.805382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.740840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.519564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.330825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.268245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:28.040312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.892637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:31.995159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:34.343634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:36.162561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.969871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.817096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.597850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.409187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.343168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:28.300859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:30.082033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:32.153287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:34.521068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:36.339001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:38.197845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.894916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.678985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.485908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.559224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:28.521740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:30.264363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:32.401501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:34.697471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:36.542243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:38.373438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:23.971191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.759003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.562047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.759951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:28.659786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:30.465830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:32.603589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:34.879156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:36.698763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:38.534214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.052578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.840927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.643255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:26.957130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:28.854365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:30.745446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:32.814067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.081008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:36.831357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:38.700484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.124203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.933506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.715394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:27.159769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.075941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:30.960766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:33.003785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.237514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.017972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:38.866251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:24.207276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.017598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:25.791483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:27.314119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:29.257238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:31.128268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:33.156364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:35.375565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-31T21:48:37.171033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-31T21:48:39.083163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-31T21:48:39.509480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idsubscription_durationrecent_login_timeaverage_login_timeaverage_time_per_learning_sessionmonthly_active_learning_daystotal_completed_coursesrecent_learning_achievementabandoned_learning_sessionscommunity_engagement_levelpreferred_difficulty_levelsubscription_typecustomer_inquiry_historypayment_patterntarget
0b919c29d131414.9461638.427187181668.36045534LowBasic450
1a0a60abb161818.45322472.646087161397.56732223MediumBasic161
2b9f171ae22116.19522821.774492131494.35876334MediumPremium071
35dc0ba8b11917.62865642.659066191870.15322803LowBasic101
465c836544521.39065630.744287191081.91790824MediumBasic301
5b7586b824417.27400734.03418921874.37102824HighBasic570
6682ca51182310.629897116.455949121058.70839002LowBasic061
78c948022102014.75171367.821393101688.56777735LowPremium111
89e6713d220412.62458237.3842108863.62439544HighBasic001
90eac44aa22810.54553618.69627351057.64699254MediumBasic471
user_idsubscription_durationrecent_login_timeaverage_login_timeaverage_time_per_learning_sessionmonthly_active_learning_daystotal_completed_coursesrecent_learning_achievementabandoned_learning_sessionscommunity_engagement_levelpreferred_difficulty_levelsubscription_typecustomer_inquiry_historypayment_patterntarget
9990365dc677222016.10476233.315978111755.93537825LowPremium251
99916ab018ed122512.73069258.4070282864.61419613HighBasic361
99925bb8044919413.105111100.63034011282.76256655LowPremium171
99931d959e9172916.16519084.130016141478.34838045LowPremium161
99949ee4e40c11611.18783688.7576106977.33591154HighBasic141
9995ae6b76bc222914.72762384.053558181664.96680325LowPremium111
999624588752101119.37405445.4648339882.75024433MediumBasic271
9997e4622a5472718.240978127.302411241481.56783935HighBasic161
9998e07fbad911718.7838005.297234101089.88565645LowBasic201
9999e12dcb5510513.07323028.12003131364.81129745LowPremium030